In farming, the various products provided for the land and crops are called “inputs”. These products can be, for example, a seed, a fertilizer, a pesticide or irrigation water. In a same farming plot, the inputting needs differ geographically, for example according to the exposure of each area, or according to the local slope etc.
In conventional farming, the provision of inputs on a farming plot is made without taking into account the variabilities inside this plot. On the other hand, precision farming consists of taking into account the variability inside a farming plot, in such a way as to optimize the quantity of inputs applied. It is understood therefore that the quantity of inputs to apply will be managed, in precision farming, in accordance with the needs of each area of a farming plot, rather than to apply a model established according to an average over the total area of the said farming plot.
As the population is being more and more demanding towards the farming profession in terms of quality, traceability and impact on the environment, precision farming has come into existence in order to answer this demand on the one hand, and on the other hand to reduce farming costs by improving the effectiveness of the inputs.
The variabilities within a farming plot are taken into account using maps, known as “yield maps”, which state the real production observed at each point of each plot, for example in the form of grain mass harvested at this point.
Today, farming equipment, for example a combine harvester, usually includes a sensor set, for example, flow sensors (pressure, optical density etc), moisture, etc, and a geolocation system, for example GPS. These sensors transmit measurements, stored in a file, aiming to be able to calculate yields at each point, these measurements can be, for example, a value relating to a flow passing at each moment through the machine, which, associated with a geolocation, constitute a set of data.
This set of data defines a flow map. The file also contains values of instantaneous yield, obtained by calculation using the instantaneous cutting width. This instantaneous yield value data is sometimes assimilated to a yield map.
However, this data includes errors related to various phenomena, for example maneuvers carried out during harvesting, of which there are various sources:
the combine harvester is completely full,
the combine harvester is stationed to empty its load of grain,
the combine harvester rotates at the end of the row,
the farming plot is sloping,
the weather changes the weight of grain observed,
the imprecision of the cutting width used,
the time lag,
the loss of GPS signal,
the blockages and/or loss of grain in the combine harvester,
the precision of the sensors.
Some of these errors are known, but correcting them remains difficult. For example, the time lag corresponds to the time interval between the time where the crop is cut at the front of the machine and the time where the grain passes in front of the flow sensor. These time lag values change in accordance with working conditions and the crop. The correction of these time lag values is predefined on leaving the factory, but most manufacturers allow the operator to change this time lag.
Cartography software which refines instant yield data is already available, for example by filtering extreme values in order to correct the data of these maps, which is, in general, assimilated to yields.
In the current state of the art, these corrections are inadequate, and a farming producer who manages a farming plot in precision farming must correct the yield map supplied by the cartography software that he uses, based on his own knowledge of the farming plot. The time spent achieving this correction is considerable, and the result still includes errors. Moreover, this data calculated from instantaneous yield, being in the current state of the art and of the market, directly assimilated to yield data, the final map cannot show the variability of the yield in a very true way.